Why ChatGPT Is Bad at Generating SQL Seed Data (And What to Use Instead)

The ChatGPT SQL Seed Data Problem

Many developers try using ChatGPT to generate SQL seed data, but they quickly discover it's not up to the task. Here's why:

❌ No Referential Integrity

ChatGPT generates data row by row without understanding relationships. It might create an order with a user_id that doesn't exist, breaking foreign key constraints.

❌ Duplicate Values

ChatGPT doesn't track what it's already generated. You'll get duplicate emails, usernames, and other values that violate unique constraints.

❌ Invalid Data Formats

ChatGPT might generate dates in the wrong format, invalid UUIDs, or JSONB that doesn't match your schema. It doesn't understand database-specific requirements.

❌ Slow for Large Datasets

Generating 10,000+ rows with ChatGPT is painfully slow and often hits token limits. You'll need multiple prompts and manual copy-pasting.

Real Example: ChatGPT vs MockBlast

Let's say you need to seed a users table and an orders table with a foreign key relationship:

ChatGPT's Approach:

-- ChatGPT generates this:
INSERT INTO users (id, email) VALUES 
  (1, 'user@example.com'),
  (2, 'user@example.com'),  -- Duplicate!
  (3, 'test@test.com');

INSERT INTO orders (id, user_id, total) VALUES
  (1, 1, 100.00),
  (2, 999, 200.00),  -- user_id doesn't exist!
  (3, 1, 150.00);

❌ Duplicate emails violate unique constraint
❌ user_id 999 doesn't exist, violates foreign key

MockBlast's Approach:

-- MockBlast generates this:
INSERT INTO users (id, email) VALUES 
  (1, 'john.doe@example.com'),
  (2, 'jane.smith@example.com'),
  (3, 'bob.johnson@example.com');

INSERT INTO orders (id, user_id, total) VALUES
  (1, 1, 99.99),
  (2, 2, 149.50),
  (3, 1, 75.25);

✓ Unique emails
✓ All user_ids reference existing users
✓ Realistic data formats

Why MockBlast Is Better for SQL Seed Data

🗄️ Schema-Aware Generation

MockBlast parses your CREATE TABLE statements and understands constraints, foreign keys, and data types. It generates data that always complies with your schema.

🔗 Automatic Foreign Key Handling

MockBlast maintains referential integrity automatically. When generating orders, it only uses user_ids that exist in the users table.

✨ Realistic Data

Uses proven data libraries (Faker.js) to generate realistic names, emails, addresses, and more. Not random strings like ChatGPT often produces.

⚡ Fast & Scalable

Generate millions of rows in seconds with server-side streaming. No token limits, no waiting.

🎯 Purpose-Built

MockBlast is designed specifically for SQL seed data generation. It's not a general-purpose AI trying to do everything—it's a specialized tool that does one thing exceptionally well.

The Best Workflow: ChatGPT + MockBlast

Here's how to combine the best of both tools:

1.
Use ChatGPT for Schema Design: Ask ChatGPT to help you design your database schema. It's great at understanding requirements and writing CREATE TABLE statements.
2.
Import to MockBlast: Copy the CREATE TABLE statements from ChatGPT and paste them into MockBlast's SQL import feature.
3.
Generate Seed Data: Let MockBlast generate realistic, constraint-compliant seed data. It understands foreign keys, unique constraints, and data types.
4.
Download & Use: Get your SQL INSERT statements instantly and seed your database. No manual fixes needed.

When to Use ChatGPT vs MockBlast

✅ Use ChatGPT For:

• Designing database schemas
• Writing complex SQL queries
• Understanding database concepts
• Debugging SQL errors
• Learning SQL best practices

✅ Use MockBlast For:

• Generating SQL seed data
• Creating test datasets
• Seeding databases with realistic data
• Generating data with foreign keys
• Creating large datasets (10k+ rows)